# Authors:
# Wilson Rocha Lacerda Junior <wilsonrljr@outlook.com>
# License: BSD 3 clause
import numpy as np
from collections import Counter
[docs]def results(
final_model=None,
theta=None,
err=None,
n_terms=None,
theta_precision=4,
err_precision=8,
dtype="dec",
):
"""Write the model regressors, parameters and ERR values.
This function returns the model regressors, its respectives parameter
and ERR value on a string matrix.
Parameters
----------
theta_precision : int (default: 4)
Precision of shown parameters values.
err_precision : int (default: 8)
Precision of shown ERR values.
dtype : string (default: 'dec')
Type of representation:
sci - Scientific notation;
dec - Decimal notation.
Returns
-------
output_matrix : string
Where:
First column represents each regressor element;
Second column represents associated parameter;
Third column represents the error reduction ratio associated
to each regressor.
"""
if not isinstance(theta_precision, int) or theta_precision < 1:
raise ValueError(
"theta_precision must be integer and > zero. Got %f" % theta_precision
)
if not isinstance(err_precision, int) or err_precision < 1:
raise ValueError(
"err_precision must be integer and > zero. Got %f" % err_precision
)
if dtype not in ("dec", "sci"):
raise ValueError("dtype must be dec or sci. Got %s" % dtype)
output_matrix = []
theta_output_format = "{:." + str(theta_precision)
err_output_format = "{:." + str(err_precision)
if dtype == "dec":
theta_output_format = theta_output_format + "f}"
err_output_format = err_output_format + "f}"
else:
theta_output_format = theta_output_format + "E}"
err_output_format = err_output_format + "E}"
for i in range(0, n_terms):
if np.max(final_model[i]) < 1:
tmp_regressor = str(1)
else:
regressor_dic = Counter(final_model[i])
regressor_string = []
for j in range(0, len(list(regressor_dic.keys()))):
regressor_key = list(regressor_dic.keys())[j]
if regressor_key < 1:
translated_key = ""
translated_exponent = ""
else:
delay_string = str(
int(regressor_key - np.floor(regressor_key / 1000) * 1000)
)
if int(regressor_key / 1000) < 2:
translated_key = "y(k-" + delay_string + ")"
else:
translated_key = (
"x"
+ str(int(regressor_key / 1000) - 1)
+ "(k-"
+ delay_string
+ ")"
)
if regressor_dic[regressor_key] < 2:
translated_exponent = ""
else:
translated_exponent = "^" + str(regressor_dic[regressor_key])
regressor_string.append(translated_key + translated_exponent)
tmp_regressor = "".join(regressor_string)
current_parameter = theta_output_format.format(theta[i, 0])
current_err = err_output_format.format(err[i])
current_output = [tmp_regressor, current_parameter, current_err]
output_matrix.append(current_output)
return output_matrix
